Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An introduction to variable and feature selection
The Journal of Machine Learning Research
Online Selecting Discriminative Tracking Features Using Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum reference set based feature selection for small sample classifications
Proceedings of the 24th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online updating appearance generative mixture model for meanshift tracking
Machine Vision and Applications
Collaborative object tracking model with local sparse representation
Journal of Visual Communication and Image Representation
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In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishable subset for tracking. In the paper, we use a special tree to formalize the feature subset space. Then conditional entropy is used to evaluating feature subset and a simple but efficient greedy search algorithm is developed to search this tree to obtain the optimal k-feature subset quickly. Furthermore, our online k-feature subset selection method is integrated into particle filter for robust tracking. Extensive experiments demonstrate that k-feature subset selected by our method is more discriminative and thus can improve tracking performance considerably.